We explore the use of the landing page content in sponsored search ad selection. Specifically, we compare the use of the ad's intrinsic content to augmenting the ad with the whole, or parts, of the landing page. We explore two types of extractive summarization techniques to select useful regions from the landing pages: out-of-context and in-context methods. Out-of-context methods select salient regions from the landing page by analyzing the content alone, without taking into account the ad associated with the landing page. In-context methods use the ad context (including its title, creative, and bid phrases) to help identify regions of the landing page that should be used by the ad selection engine. In addition, we introduce a simple yet effective unsupervised algorithm to enrich the ad context to further improve the ad selection. Experimental evaluation confirms that the use of landing pages can significantly improve the quality of ad selection. We also find that our extractive summarization techniques reduce the size of landing pages substantially, while retaining or even improving the performance of ad retrieval over the method that utilize the entire landing page.

Inverted indexes have become the standard indexing method for supporting search queries in a variety of content-based applications. Examples of such applications include enterprise document management, e-mail, web search, and social networks. One shortcoming in current inverted index designs is that they support only document-level updates, forcing a full document to be reindexed even if just part of it changes. This paper describes a new inverted index design that enables applications to break a document into semantically meaningful sub-documents or "sections". Each section of a document can be updated separately, but search queries can still work seamlessly across sections. Our index design is motivated by applications where there is metadata associated with each document that tends to be smaller and more frequently updated than the document's content, but at the same time, it is desireable to search the metadata and content with the same index structure. A novel self-optimizing query execution algorithm is described to efficiently join the sections of a document in the inverted index. Experimental results on TREC and patent data are provided, showing that sections can dramatically improve overall system throughput on a mixed workload of updates and queries.